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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Chen Liu

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Fast Adversarial Training With Adaptive Step Size

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Robust Binary Models by Pruning Randomly-initialized Networks

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Simultaneous dual-contrast three-dimensional imaging in live cells via optical diffraction tomography and fluorescence

On Certifying Non-Uniform Bounds against Adversarial Attacks

Finding Mixed Nash Equilibria of Generative Adversarial Networks

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Fast Adversarial Training With Adaptive Step Size

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Simultaneous dual-contrast three-dimensional imaging in live cells via optical diffraction tomography and fluorescence

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Robust Binary Models by Pruning Randomly-initialized Networks

Finding Mixed Nash Equilibria of Generative Adversarial Networks

On Certifying Non-Uniform Bounds against Adversarial Attacks